Book Image

Building Big Data Pipelines with Apache Beam

By : Jan Lukavský
Book Image

Building Big Data Pipelines with Apache Beam

By: Jan Lukavský

Overview of this book

Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing. This book will help you to confidently build data processing pipelines with Apache Beam. You’ll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You’ll also learn how to test and run the pipelines efficiently. As you progress, you’ll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you’ll understand advanced Apache Beam concepts, such as implementing your own I/O connectors. By the end of this book, you’ll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.
Table of Contents (13 chapters)
1
Section 1 Apache Beam: Essentials
5
Section 2 Apache Beam: Toward Improving Usability
9
Section 3 Apache Beam: Advanced Concepts

Table-stream duality

We will conclude this chapter with something that should already feel natural but is worth noting explicitly – that is, table-stream duality. We will use this concept in the next chapter, but because we have already worked with primary keys, join keys, and values, the definition naturally fits into this chapter.

We have seen two types of streams supporting deletions – upsert streams and retract streams. The main difference is that the upsert stream has an explicit primary key, while the retract stream can contain exact duplicates. Let's define a specific reduction operation for each of these streams and see what would happen if we were to apply it to these particular streams:

  • If the stream is a retract stream, then in addition, simply add the input element to a list and on retraction, find the matching element in the list and remove it.
  • If the stream is an upsert stream, keep the data in a map with a key that's the primary...